A Data Science Project- Part 1(b)

In the previous article (https://d4datascience.wordpress.com/2016/11/10/a-data-science-project-part-1/), we have done basic data analysis like calculating means, frequency tables, summary etc. Now we will derive new variables. Why?
Derived variables will help to understand more about them. For example, We have derived variable ip(derived from incomeperperson variable) which will help us to understand how many people fall in lower income class or higher income class. Similarly other variables le, ac transformed into new variables.
How do we decide these value of cutoff points?
This is answered by the business people or you have to explore the data to divide them into different buckets.

If you have any query, let me know in comment section.

A Data Science Project- Part 1(a)

In the earlier post, we have discussed our hypothesis and different variables impacting life expectancy. In this post, we will start digging into data. I suggest you go through codebook(check the earlier post). It is important to understand variables first before doing any analysis. I have chosen SAS as data analysis language, but you are free to choose any language R or Python. The process and logic will be same, the just syntax will be different. Now in this part, we will explore all the variables. We will check frequency and distribution of variables. For numeric variables, you can draw the histogram, boxplot, quantiles to understand them and for the categorical variables, you can draw barplot, frequency tables.